Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction

BMC Bioinformatics

Volumn 14, Issue 1, 2013, Pages

  Stojanova, Daniela ^a   Ceci, Michelangelo ^b   Malerba, Donato ^b   Dzeroski, Saso ^a,c  

^a JO EF STEFAN INSTITUTE   (Slovenia)

^b UNIVERSITY OF BARI   (Italy)

^c Centre of Excellence for Integrated Approaches in Chemistry and Biology of Proteins   (Slovenia)

Author keywords

[No Author keywords available]

Indexed keywords

FUNCTIONAL ANNOTATION; GENE FUNCTION PREDICTION; HIERARCHICAL MULTI-LABEL CLASSIFICATIONS; HIERARCHICAL ORGANIZATIONS; INDEPENDENTLY AND IDENTICALLY DISTRIBUTED; PREDICTIVE PERFORMANCE; PROTEIN-PROTEIN INTERACTION NETWORKS; TREE-BASED ALGORITHMS;

AUTOCORRELATION; CLASSIFICATION (OF INFORMATION); DATA PROCESSING; GENES; LEARNING ALGORITHMS; LEARNING SYSTEMS; PROTEINS;

COMPLEX NETWORKS;

SACCHAROMYCES CEREVISIAE PROTEIN;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; BIOLOGY; CLASSIFICATION; GENE ONTOLOGY; GENETIC DATABASE; GENETICS; METABOLISM; METHODOLOGY; MOLECULAR GENETICS; PROTEIN PROTEIN INTERACTION;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; DATABASES, GENETIC; GENE ONTOLOGY; MOLECULAR SEQUENCE ANNOTATION; PROTEIN INTERACTION MAPS; SACCHAROMYCES CEREVISIAE PROTEINS;

EID: 84884553372     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-14-285     Document Type: Article

References (51)

1. Clare A, King RD. Predicting gene function in Saccharomyces cerevisiae. Bioinformatics 2003, 19(Suppl 2):ii42-ii49. 10.1093/bioinformatics/btg1058, 14534170.
2. Qi Y, Noble W. Protein interaction networks: protein domain interaction and protein function prediction. Handbook of Computational Statistics: Statistical Bioinformatics 2011, Heidelberg: Springer-Verlag, Lu HH, Scholkopf B, Zhao H.
3. Radivojac P, et al. A large-scale evaluation of computational protein function prediction. Nat Methods 2013, 10(3):221-227. 10.1038/nmeth.2340, 3584181, 23353650.
4. Vens C, Struyf J, Schietgat L, Džeroski S, Blockeel H. Decision trees for hierarchical multi-label classification. Mach Learn 2008, 73(2):185-214.
5. Jiang X, Nariai N, Steffen M, Kasif S, Kolaczyk E. Integration of relational and hierarchical network information for protein function prediction. BMC Bioinformatics 2008, 9(350).
6. Ashburner M, et al. Gene ontology: tool for the unification of biology. The gene ontology consortium. Nat Genet 2000, 25:25-29. 10.1038/75556, 3037419, 10802651.
7. Mewes H, Heumann K, Kaps A, Mayer K, Pfeiffer F, Stocker S, Frishman D. MIPS: A database for protein sequences and complete genomes. Nucleic Acids Res 1999, 27:44-48. 10.1093/nar/27.1.44, 148093, 9847138.
8. Barutcuoglu Z, Schapire RE, Troyanskaya OG. Hierarchical multi-label prediction of gene function. Bioinformatics 2006, 22(7):830-836. 10.1093/bioinformatics/btk048, 16410319.
9. Cressie N. Statistics for Spatial Data 1993, New York: Wiley.
10. Horne D. Prediction of protein helix content from an autocorrelation analysis of sequence hydrophobicities. Biopolymers 1988, 27:451-477. 10.1002/bip.360270308, 3359010.
11. Blockeel H, De Raedt L, Ramon J. Top-down induction of clustering trees. Proc. 15th Intl. Conf. on Machine Learning 1998, 55-63. San Francisco: Morgan Kaufmann.
12. Stojanova D, Ceci M, Appice A, Džeroski S. Network regression with predictive clustering trees. Data Mining Knowl Discov 2012, 25(2):378-413.
13. Neville J, Simsek O, Jensen D. Autocorrelation and relational learning: challenges and opportunities. Proc. Wshp. Statistical Relational Learning 2004,
14. Angin P, Neville J. A shrinkage approach for modeling non-stationary relational autocorrelation. Proc. 8th Intl. Conf. on Data Mining 2008, 707-712. Washington D.C.: IEEE Computer Society.
15. Stojanova D, Ceci M, Malerba D, Džeroski S. Learning hierarchical multi-label classification trees from network data. Discovery Science, Volume 8140 of Lecture Notes in Computer Science 2013, 233-248. Heidelberg: Springer, Fürnkranz J, Hüllermeier E, Higuchi T.
16. Quinlan RJ. C4.5: Programs for Machine Learning 1993, San Francisco: Morgan Kauffmann.
17. Schietgat L, Vens C, Struyf J, Blockeel H, Kocev D, Džeroski S. Predicting gene function using hierarchical multi-label decision tree ensembles. BMC Bioinformatics 2010, 11(2).
18. Steinhaeuser K, Chawla NV, Ganguly AR. Complex networks as a unified framework for descriptive analysis and predictive modeling in climate science. Stat Anal Data Mining 2011, 4(5):497-511.
19. Doreian P. Network autocorrelation models: Problems and prospects. Spatial Statistics: Past, Present, and Future Ann Arbor: Ann Arbor Institute of Mathematical Geography; 1990.
20. Gillis J, Pavlidis P. "Guilt by Association" is the exception rather than the rule in gene networks. PLoS Comput Biol 2012, 8(3):e1002444+. 3315453, 22479173.
21. Legendre P. Spatial autocorrelation: trouble or new paradigm?. Ecology 1993, 74(6):1659-1673.
22. Stojanova D, Ceci M, Appice A, Malerba D, Dzeroski S. Dealing with spatial autocorrelation when learning predictive clustering trees. Ecol Inform 2013, 13:22-39.
23. Mehta M, Agrawal R, Rissanen J. SLIQ: A fast scalable classifier for data mining. Proc. 5th Intl. Conf. Extending Database Technology 1996, 18-32. Heidelberg: Springer-Verlag, [Lecture Notes in Computer Science, volume 1057].
24. Ouali M, King R. Cascaded multiple classifiers for secondary structure prediction. Protein Sci 2000, 9(6):1162-1176. 10.1110/ps.9.6.1162, 2144653, 10892809.
25. Altschul S, Madden T, Schaffer A, Zhang J, Zhang Z, Miller W, Lipman D. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res 1997, 25:3389-3402. 10.1093/nar/25.17.3389, 146917, 9254694.
26. et al. Ruepp The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes. Nucleic Acids Res 2004, 32(18):5539-5545. 10.1093/nar/gkh894, 524302, 15486203, Ruepp.
27. Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M. BioGRID: a general repository for interaction datasets. Nucleic Acids Res 2006, 34(suppl 1):D535-D539. 1347471, 16381927.
28. Deane CM, Salwiński T, Xenarios I, Eisenberg D. Protein interactions. Mol Cell Proteomic 2002, 1(5):349-356.
29. Nabieva E, Jim K, Agarwal A, Chazelle B, Singh M. Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps. Bioinformatics 2005, 21:302-310.
30. Karaoz U, Murali TM, Letovsky S, Zheng Y, Ding C, Cantor CR, Kasif S. Whole-genome annotation by using evidence integration in functional-linkage networks. Proc Natl Acad Sci USA 2004, 101:2888-2893. 10.1073/pnas.0307326101, 365715, 14981259.
31. Hughes G. On the mean accuracy of statistical pattern recognizers. IEEE Trans Inf Theory 1968, 14:55-63.
32. Zhao Z, Liu H. Spectral feature selection for supervised and unsupervised learning. ICML, Volume 227 of ACM International Conference Proceeding Series 2007, 1151-1157. New York: ACM, Ghahramani Z.
33. Appice A, Ceci M, Rawles S, Flach PA. Redundant feature elimination for multi-class problems. ICML, Volume 69 of ACM International Conference Proceeding Series 2004, New York: ACM, Brodley CE.
34. Cerri R, Barros RC, de Carvalho ACPLF. A genetic algorithm for hierarchical multi-label classification. Proc. 27th Annual ACM Symposium on Applied Computing 2012, 250-255. New York: ACM.
35. Shi L, Lei X, Zhang A. Protein complex detection with semi-supervised learning in protein interaction networks. Proteome Sci 2011, 9:41-42. 10.1186/1477-5956-9-41, 3162492, 21791079.
36. Pavlidis P, Weston J, Cai J, Noble WS. Learning gene functional classifications from multiple data types. J Comput Biol 2002, 9(2):401-411. 10.1089/10665270252935539, 12015889.
37. Pandey G, Kumar V, Steinbach M. Computational approaches for protein function prediction. Tech. Rep. TR 06-028, Department of Computer Science and Engineering, University of Minnesota, Twin Cities 2006.
38. Eisner R, Poulin B, Szafron D, Lu P, Greiner R. Improving protein function prediction using the hierarchical structure of the Gene Ontology. Proc. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology 2005, Washington D.C.: IEEE Computer Society.
39. Shahbaba B, Neal RM. Gene function classification using Bayesian models with hierarchy-based priors. BMC Bioinformatics 2006, 7:448. 10.1186/1471-2105-7-448, 1618412, 17038174.
40. Sokolov A, Ben-Hur A. Hierarchical classification of gene ontology terms using the GOstruct method. J Bioinformatics Comput Biol 2010, 8(2):357-376.
41. Astikainen K, Pitkänen E, Rousu J, Holm L, Szedmák S. Reaction kernels - structured output prediction approaches for novel enzyme function. Bioinformatics 2010, 48-55.
42. Valentini G. True path rule hierarchical ensembles for genome-wide gene function prediction. IEEE ACM Trans Comput Biol Bioinformatics 2010, 8(3):832-847.
43. Sharan R, Ulitsky I, Shamir R. Network-based prediction of protein function. Mol Syst Biol 2007, 3(88).
44. Wang PI, Marcott EM. It's the machine that matters: Predicting gene function and phenotype from protein networks. J Proteomic 2010, 73(11):2277-2289.
45. Letovsky S, Kasif S. Predicting protein function from protein/protein interaction data: a probabilistic approach. Bioinformatics 2003, 19(suppl 1):i197-i204.
46. Vazquez A, Flammini A, Maritan A, Vespignani A. Global protein function prediction from protein-protein interaction networks. Nat Biotechnol 2003, 21(6):697-700. 10.1038/nbt825, 12740586.
47. Nariai N, Kolaczyk E, Kasif S. Probabilistic protein function prediction from feterogeneous genome-wide data. PLoS ONE 2007, 2(3):e337. 10.1371/journal.pone.0000337, 1828618, 17396164.
48. Milenkovic T, Przulj N. Uncovering biological network function via graphlet degree signatures. Cancer Inform 2008, 6:257-273. 2623288, 19259413.
49. Borgwardt KM, Ong CS, Schönauer S, Vishwanathan SVN, Smola AJ, Kriegel HP. Protein function prediction via graph kernels. Bioinformatics 2005, 21(suppl 1):i47-i56.
50. Tao Y, Sam L, Li J, Friedman C, Lussier YA. Information theory applied to the sparse gene ontology annotation network to predict novel gene function. Bioinformatics 2007, 23(13):i529-i538. 2882681, 17646340.
51. Pandey G, Myers C, Kumar V. Incorporating functional inter-relationships into protein function prediction algorithms. BMC Bioinformatics 2009, 10(142).